Context engineering for multi ai systems guide (18 résultats)

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Etat : As New. Unread book in perfect condition.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. Conversational AI is moving beyond single chatbots and into collaborative multi-agent systems, networks of intelligent agents that work together, communicate, and solve complex tasks in real time. This book gives developers and testers the practical knowledge needed to design, build, and deploy such systems with confidence.Written with a focus on hands-on implementation, it shows how to move from theory to working prototypes, covering everything from agent communication models to workflow orchestration, memory design, and real-world applications. Developers will learn how to integrate frameworks and tools into robust systems, while testers will gain methods for validating reliability, scalability, and collaborative behavior.Key takeaways include: How conversational multi-agent systems differ from traditional chatbotsArchitectures for coordination, delegation, and shared memoryPractical code examples in Python and leading frameworksTesting strategies for ensuring agent collaboration and performanceUse cases across software development, customer support, and automationWhether you're building your first agentic system or refining production workflows, this book gives you a clear, practical guide to making multi-agent AI conversational, reliable, and effective.For developers and testers ready to move from isolated agents to collaborative AI systems that scale, this is your playbook. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflow in AI Agents is a practical guide for developers, researchers, and system architects who want to design intelligent, reliable, and scalable agentic AI systems.Modern AI agents cannot function effectively without well-structured context. From memory persistence to communication protocols, from orchestrating multi-agent workflows to integrating retrieval-augmented knowledge, context engineering is the backbone that determines whether agents collaborate productively-or collapse under complexity.This book provides a hands-on, implementation-focused framework for mastering context in multi-agent systems. Readers will learn: How to design short-term and long-term memory architectures for agents.Strategies for communication across distributed agents, including message passing, routing, and shared state.Practical approaches to building scalable workflows that integrate both SLMs and LLMs for cost-efficient reasoning.Methods for connecting vector databases, retrieval-augmented generation (RAG), and external tools into multi-agent pipelines.Debugging, optimization, and best practices for real-world deployment.Packed with practical insights, annotated code examples, and actionable patterns, this book bridges the gap between conceptual AI frameworks and production-ready systems. Whether you are an AI developer, researcher, or product engineer, you will gain the skills to optimize memory, communication, and orchestration in your agent workflows.If you're ready to go beyond surface-level orchestration and master the real engine that drives intelligent agents-contex-this book will give you the blueprint. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflow in AI Agents is a practical guide for developers, researchers, and system architects who want to design intelligent, reliable, and scalable agentic AI systems.Modern AI agents cannot function effectively without well-structured context. From memory persistence to communication protocols, from orchestrating multi-agent workflows to integrating retrieval-augmented knowledge, context engineering is the backbone that determines whether agents collaborate productively-or collapse under complexity.This book provides a hands-on, implementation-focused framework for mastering context in multi-agent systems. Readers will learn: How to design short-term and long-term memory architectures for agents.Strategies for communication across distributed agents, including message passing, routing, and shared state.Practical approaches to building scalable workflows that integrate both SLMs and LLMs for cost-efficient reasoning.Methods for connecting vector databases, retrieval-augmented generation (RAG), and external tools into multi-agent pipelines.Debugging, optimization, and best practices for real-world deployment.Packed with practical insights, annotated code examples, and actionable patterns, this book bridges the gap between conceptual AI frameworks and production-ready systems. Whether you are an AI developer, researcher, or product engineer, you will gain the skills to optimize memory, communication, and orchestration in your agent workflows.If you're ready to go beyond surface-level orchestration and master the real engine that drives intelligent agents-contex-this book will give you the blueprint. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. Conversational AI is moving beyond single chatbots and into collaborative multi-agent systems, networks of intelligent agents that work together, communicate, and solve complex tasks in real time. This book gives developers and testers the practical knowledge needed to design, build, and deploy such systems with confidence.Written with a focus on hands-on implementation, it shows how to move from theory to working prototypes, covering everything from agent communication models to workflow orchestration, memory design, and real-world applications. Developers will learn how to integrate frameworks and tools into robust systems, while testers will gain methods for validating reliability, scalability, and collaborative behavior.Key takeaways include: How conversational multi-agent systems differ from traditional chatbotsArchitectures for coordination, delegation, and shared memoryPractical code examples in Python and leading frameworksTesting strategies for ensuring agent collaboration and performanceUse cases across software development, customer support, and automationWhether you're building your first agentic system or refining production workflows, this book gives you a clear, practical guide to making multi-agent AI conversational, reliable, and effective.For developers and testers ready to move from isolated agents to collaborative AI systems that scale, this is your playbook. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…